AI RESEARCH
Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
arXiv CS.CV
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ArXi:2512.08410v2 Announce Type: replace Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient paradigm to remedy this shortcoming, termed One-shot video-Clip based Retrieval-Augmented Generation (OneClip-RAG). Compared with existing video RAG methods, OneClip-RAG makes full use of the merits of video clips for augmented video understanding in terms of both knowledge integrity and semantic coherence.